Last updated: 2023-12-19

Checks: 4 3

Knit directory: ILD_ASE_Xenium/

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Environment: objects present

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The following objects were defined in the global environment when these results were created:

Name Class Size
cluster_col character 2.8 Kb
clusters numeric 216 bytes
create_barplot function 33.4 Kb
create_clusterpropplot function 37.2 Kb
create_dotplot_heatmap function 74.1 Kb
create_dotplot_heatmap_horizontal function 53.4 Kb
ct_annot googlesheets4_spreadsheet;list 4.3 Kb
endo_mesen Seurat 1.2 Gb
endo_mesen_clusters numeric 80 bytes
endo_mesen_merged Seurat 145.6 Mb
endo_mesen_reclustered Seurat 4.5 Gb
endo_mesen_subsets list 2.7 Gb
endothelial_features character 1.3 Kb
epi_annot tbl_df;tbl;data.frame 3.5 Kb
epi_celltype_col character 48 bytes
epi_clusters numeric 80 bytes
epithelial Seurat 700.8 Mb
epithelial_features character 3.7 Kb
epithelial_merged Seurat 301.7 Mb
epithelial_reclustered Seurat 2.2 Gb
epithelial_subsets list 1.7 Gb
get_pcs function 73.6 Kb
imm_clusters numeric 80 bytes
immune Seurat 232.9 Mb
immune_features character 5.3 Kb
immune_merged Seurat 658.7 Mb
immune_reclustered Seurat 594.7 Mb
immune_subsets list 3.5 Gb
lineage_col character 768 bytes
mesenchymal_features character 1.1 Kb
recluster function 111.4 Kb
sample_col character 4.3 Kb
sample_type character 176 bytes
sample_type_col character 480 bytes
samples character 2.1 Kb
seurat_object Seurat 2.2 Gb

Seed: set.seed(20231206)

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/home/hnatri/ILD_ASE_Xenium/ .
/home/hnatri/ILD_ASE_Xenium/code/colors_themes.R code/colors_themes.R
/home/hnatri/ILD_ASE_Xenium/code/plot_functions.R code/plot_functions.R
/home/hnatri/ILD_ASE_Xenium/code/utilities.R code/utilities.R

Repository version: 83e2df3

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Untracked files:
    Untracked:  analysis/endo_mesen_annotations.Rmd

Unstaged changes:
    Modified:   README.md
    Modified:   analysis/Xenium_preprocessing.Rmd
    Modified:   analysis/celltype_annotations.Rmd
    Modified:   analysis/epithelial_annotations.Rmd
    Modified:   analysis/immune_annotations.Rmd
    Modified:   analysis/index.Rmd
    Modified:   code/colors_themes.R
    Modified:   code/utilities.R

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File Version Author Date Message
Rmd 83e2df3 heinin 2023-12-19 Adding lineage level annotations
html 83e2df3 heinin 2023-12-19 Adding lineage level annotations

First pass annotations on the epithelial lineage

Packages and environment variables

suppressPackageStartupMessages({library(cli)
                                library(Seurat)
                                library(SeuratObject)
                                library(SeuratDisk)
                                library(tidyverse)
                                library(tibble)
                                library(plyr)
                                library(ggplot2)
                                library(ggpubr)
                                library(ggrepel)
                                library(workflowr)
                                library(googlesheets4)
                                library(presto)})

setwd("/home/hnatri/ILD_ASE_Xenium/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)

# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/ILD_ASE_Xenium/code/colors_themes.R")
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Epithelial''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Immune''.
source("/home/hnatri/ILD_ASE_Xenium/code/plot_functions.R")
source("/home/hnatri/ILD_ASE_Xenium/code/utilities.R")

Import data

epithelial_merged <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_merged.rds")
DefaultAssay(epithelial_merged)
[1] "RNA"
# Adding one more cluster from the annotated immune object
immune_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered_annotated_orig.rds")

epithelial_merged <- merge(epithelial_merged,
                           subset(immune_reclustered,
                                  subset = annotation_1 == "Epithelial"))
Warning: Some cell names are duplicated across objects provided. Renaming to
enforce unique cell names.

Redonstructing the dimensionality reduction and neighbor graph

epithelial_reclustered <- recluster(epithelial_merged)
# PCs for UMAP: 11
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
DimPlot(epithelial_reclustered,
        group.by = "snn_res.0.8",
        reduction = "umap",
        raster = T,
        cols = cluster_col,
        label = T) +
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Version Author Date
83e2df3 heinin 2023-12-19 
DimPlot(epithelial_reclustered,
        group.by = "snn_res.0.8",
        split.by = "snn_res.0.8",
        ncol = 4,
        reduction = "umap",
        raster = T,
        cols = cluster_col) +
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Version Author Date
83e2df3 heinin 2023-12-19 
# Numbers of cells per cluster
table(epithelial_reclustered$snn_res.0.8) %>% as.data.frame() %>%
  ggplot(aes(x = Var1, y = Freq)) +
  geom_bar(stat="identity", fill = "gray89") +
  xlab("snn_res.0.5") +
  ylab("# cells") +
  theme_minimal()

Marker expression

# Checking some lineage markers
FeaturePlot(epithelial_reclustered,
            features = c("EPCAM", "AGER", "CD3E", "CD19", "CD34", "SFRP2"),
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Version Author Date
83e2df3 heinin 2023-12-19 
# AT1 and AT2 markers
# AT1: Hopx, Pdpn, and Ager
# AT2: Sftpb, Sftpc, and Sftpd
# Transitional AT2: SFTPC, AGER
# Proliferating: MKI67, CDK1
# Basal: FOXI1
# Ciliated: SFTPB, FOXJ1

FeaturePlot(epithelial_reclustered,
            features = c("HOPX", "PDPN", "AGER", "SFTPB", "SFTPC", "SFTPD",
                         "FOXJ1", "FOXI1", "MKI67", "CDK1"),
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Warning: The following requested variables were not found: HOPX, SFTPB, SFTPC
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Version Author Date
83e2df3 heinin 2023-12-19 
# All epithelial markers
FeaturePlot(epithelial_reclustered,
            features = epithelial_features,
            ncol = 6,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DotPlot(epithelial_reclustered,
        features = epithelial_features,
        group.by = "snn_res.0.5",
        cols = c("azure", "tomato3")) +
  coord_flip() +
  theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

Top markers for each cluster

# Only looking at base panel features
features <- rownames(epithelial_reclustered)
features <- features[-grep("-", features)]
base_counts <- LayerData(epithelial_reclustered,
                         assay = "RNA",
                         layer = "counts")
base_counts <- base_counts[features, ]

# Creating a new assay
epithelial_reclustered[["base_RNA"]] <- CreateAssay5Object(counts = base_counts)
Warning: Different cells and/or features from existing assay base_RNA
DefaultAssay(epithelial_reclustered) <- "base_RNA"
epithelial_reclustered <- NormalizeData(epithelial_reclustered,
                                        assay = "base_RNA",
                                        normalization.method = "LogNormalize",
                                        verbose = F)
Warning: The following arguments are not used: layer
# Comparing Seurat and presto
Idents(epithelial_reclustered) <- epithelial_reclustered$snn_res.0.8
cluster_markers <- FindAllMarkers(epithelial_reclustered,
                                  assay = "base_RNA",
                                  logfc.threshold = 0.25,
                                  test.use = "wilcox",
                                  slot = "data",
                                  min.pct = 0.1,
                                  verbose = F)
cluster_markers_presto <- presto::wilcoxauc(epithelial_reclustered,
                                            group_by = "snn_res.0.8",
                                            assay = "data",
                                            seurat_assay = "base_RNA")

# Overlap of top markers for cluster 0
cluster_markers_sig <- cluster_markers %>%
  filter(p_val_adj<0.01, abs(avg_log2FC)>0.5) %>%
  filter(cluster==0) %>%
  select(gene) %>% unlist() %>% as.character()
  
cluster_markers_presto_sig <- cluster_markers_presto %>%
  filter(padj<0.01, abs(logFC)>0.5) %>%
  filter(group==0) %>%
  select(feature) %>% unlist() %>% as.character()

length(cluster_markers_sig)
[1] 44
length(cluster_markers_presto_sig)
[1] 37
length(intersect(cluster_markers_sig, cluster_markers_presto_sig))
[1] 36
setdiff(cluster_markers_sig, cluster_markers_presto_sig)
[1] "PIM2"   "GLIPR2" "CD38"   "CFTR"   "CYP2F1" "MYO6"   "DNAJB9" "HMGCS1"
setdiff(cluster_markers_presto_sig, cluster_markers_sig)
[1] "CDH1"
# Selecting top 8 markers for each cluster
cluster_markers_sig <- cluster_markers %>%
  filter(p_val_adj<0.01, abs(avg_log2FC)>0.5) %>%
  group_by(cluster) %>%
  slice_max(order_by = abs(avg_log2FC), n = 8) %>%
  ungroup %>% select(gene) %>% unlist() %>% as.character() %>% unique()
# Heatmap of top markers
# seurat_object = Seurat object with all features normalized and scaled
# plot_features = a vector a features to plot
# group_var = e.g. cluster
# group_colors = named vector of colors
# column_title = plot title
hm <- create_dotplot_heatmap(seurat_object = epithelial_reclustered,
                             plot_features = cluster_markers_sig,
                             group_var = "snn_res.0.8",
                             group_colors = cluster_col,
                             column_title = "Epithelial")

Read cluster annotations from a Google Sheet

gs4_deauth()
ct_annot  <- gs4_get("https://docs.google.com/spreadsheets/d/1SDfhxf6SjllxXEtNPf32ZKTEqHC9QJW3BpRYZFhpqFE/edit?usp=sharing")
sheet_names(ct_annot)
[1] "Full object, 20 PCs, leiden_res0.5" "Lineage level, reclustered"        
[3] "Epithelial"                         "Immune"                            
[5] "Endo_Mesen"                         "Endothelial"                       
[7] "Mesenchymal"                       
epi_annot <- read_sheet(ct_annot, sheet = "Epithelial")
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Epithelial''.

Adding annotations to the Seurat object and saving

epithelial_reclustered$annotation_1 <- mapvalues(x = epithelial_reclustered$snn_res.0.8,
                                                 from = epi_annot$snn_res.0.8,
                                                 to = epi_annot$annotation_1)

saveRDS(epithelial_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered_annotated.rds")

Visualizing

DimPlot(epithelial_reclustered,
        group.by = "annotation_1",
        reduction = "umap",
        raster = T,
        cols = epi_celltype_col,
        label = T,
        label.box = T,
        label.size = 3,
        repel = T) +
  coord_fixed(ratio = 1) &
  theme_minimal() &
  NoLegend()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`


sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] presto_1.0.0            data.table_1.14.8       Rcpp_1.0.10            
 [4] plyr_1.8.8              ComplexHeatmap_2.16.0   viridis_0.6.3          
 [7] viridisLite_0.4.2       RColorBrewer_1.1-3      ggthemes_5.0.0         
[10] googlesheets4_1.1.0     workflowr_1.7.1         ggrepel_0.9.3          
[13] ggpubr_0.6.0            lubridate_1.9.2         forcats_1.0.0          
[16] stringr_1.5.0           dplyr_1.1.2             purrr_1.0.1            
[19] readr_2.1.4             tidyr_1.3.0             tibble_3.2.1           
[22] ggplot2_3.4.2           tidyverse_2.0.0         SeuratDisk_0.0.0.9021  
[25] Seurat_4.9.9.9048       SeuratObject_4.9.9.9084 sp_1.6-1               
[28] cli_3.6.1              

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.20       splines_4.3.0          later_1.3.1           
  [4] cellranger_1.1.0       polyclip_1.10-4        fastDummies_1.6.3     
  [7] lifecycle_1.0.3        rstatix_0.7.2          doParallel_1.0.17     
 [10] rprojroot_2.0.3        globals_0.16.2         processx_3.8.1        
 [13] lattice_0.21-8         hdf5r_1.3.8            MASS_7.3-60           
 [16] backports_1.4.1        magrittr_2.0.3         plotly_4.10.2         
 [19] sass_0.4.6             rmarkdown_2.22         jquerylib_0.1.4       
 [22] yaml_2.3.7             httpuv_1.6.11          sctransform_0.3.5     
 [25] spam_2.9-1             spatstat.sparse_3.0-1  reticulate_1.29       
 [28] cowplot_1.1.1          pbapply_1.7-0          abind_1.4-5           
 [31] Rtsne_0.16             BiocGenerics_0.46.0    git2r_0.32.0          
 [34] circlize_0.4.15        S4Vectors_0.38.1       IRanges_2.34.0        
 [37] irlba_2.3.5.1          listenv_0.9.0          spatstat.utils_3.0-3  
 [40] goftest_1.2-3          RSpectra_0.16-1        spatstat.random_3.1-5 
 [43] fitdistrplus_1.1-11    parallelly_1.36.0      leiden_0.4.3          
 [46] codetools_0.2-19       shape_1.4.6            tidyselect_1.2.0      
 [49] farver_2.1.1           stats4_4.3.0           matrixStats_1.0.0     
 [52] spatstat.explore_3.2-1 googledrive_2.1.0      jsonlite_1.8.5        
 [55] GetoptLong_1.0.5       ellipsis_0.3.2         progressr_0.13.0      
 [58] iterators_1.0.14       ggridges_0.5.4         survival_3.5-5        
 [61] foreach_1.5.2          tools_4.3.0            ica_1.0-3             
 [64] glue_1.6.2             gridExtra_2.3          xfun_0.39             
 [67] withr_2.5.0            fastmap_1.1.1          fansi_1.0.4           
 [70] callr_3.7.3            digest_0.6.31          timechange_0.2.0      
 [73] R6_2.5.1               mime_0.12              colorspace_2.1-0      
 [76] Cairo_1.6-0            scattermore_1.1        tensor_1.5            
 [79] spatstat.data_3.0-1    utf8_1.2.3             generics_0.1.3        
 [82] httr_1.4.6             htmlwidgets_1.6.2      whisker_0.4.1         
 [85] uwot_0.1.14            pkgconfig_2.0.3        gtable_0.3.3          
 [88] lmtest_0.9-40          htmltools_0.5.5        carData_3.0-5         
 [91] dotCall64_1.0-2        clue_0.3-64            scales_1.2.1          
 [94] png_0.1-8              knitr_1.43             rstudioapi_0.14       
 [97] rjson_0.2.21           tzdb_0.4.0             reshape2_1.4.4        
[100] curl_5.0.0             nlme_3.1-162           GlobalOptions_0.1.2   
[103] cachem_1.0.8           zoo_1.8-12             KernSmooth_2.23-21    
[106] parallel_4.3.0         miniUI_0.1.1.1         pillar_1.9.0          
[109] vctrs_0.6.2            RANN_2.6.1             promises_1.2.0.1      
[112] car_3.1-2              xtable_1.8-4           cluster_2.1.4         
[115] evaluate_0.21          magick_2.7.4           compiler_4.3.0        
[118] rlang_1.1.1            crayon_1.5.2           future.apply_1.11.0   
[121] ggsignif_0.6.4         labeling_0.4.2         ps_1.7.5              
[124] getPass_0.2-2          fs_1.6.2               stringi_1.7.12        
[127] deldir_1.0-9           munsell_0.5.0          lazyeval_0.2.2        
[130] spatstat.geom_3.2-1    Matrix_1.5-4.1         RcppHNSW_0.4.1        
[133] hms_1.1.3              patchwork_1.1.2        bit64_4.0.5           
[136] future_1.32.0          shiny_1.7.4            highr_0.10            
[139] ROCR_1.0-11            fontawesome_0.5.1      gargle_1.4.0          
[142] igraph_1.4.3           broom_1.0.4            bslib_0.4.2           
[145] bit_4.0.5